On Google's ML crash course it states:
Good feature values should appear more than 5 or so times in a data set. Doing so enables a model to learn how this feature value relates to the label. That is, having many examples with the same discrete value gives the model a chance to see the feature in different settings, and in turn, determine when it's a good predictor for the label.
This make sense, but if that happens what should we do? For example, consider a dataset with
street_name as feature and out 2000 rows, only 4 of them have
street_name equal to "Learning St.".
Should we remove those rows containing rare feature values? Or what?
Any insights would be greatly appreciated.